Graph machine learning for integrated multi-omics analysis.

Multi-omics experiments at bulk or single-cell resolution facilitate the discovery of hypothesis-generating biomarkers for predicting response to therapy, as well as aid in uncovering mechanistic insights into cellular and microenvironmental processes. Many methods for data integration have been developed for the identification of key elements that explain or predict disease risk or other biological outcomes. The heterogeneous graph representation of multi-omics data provides an advantage for discerning patterns suitable for predictive/exploratory analysis, thus permitting the modeling of complex relationships.

Intestinal BMP-9 locally upregulates FGF19 and is down-regulated in obese patients with diabetes.

Bone morphogenetic protein (BMP)-9, a member of the TGFβ-family of cytokines, is believed to be mainly produced in the liver. The serum levels of BMP-9 were reported to be reduced in newly diagnosed diabetic patients and BMP-9 overexpression ameliorated steatosis in the high fat diet-induced obesity mouse model. Furthermore, injection of BMP-9 in mice enhanced expression of fibroblast growth factor (FGF)21.

Quantitative Assessment of Breast-Tumor Stiffness Using Shear-Wave Elastography Histograms.

Purpose: Shear-wave elastography (SWE) measures tissue elasticity using ultrasound waves. This study proposes a histogram-based SWE analysis to improve breast malignancy detection. Methods: N = 22/32 (patients/tumors) benign and n = 51/64 malignant breast tumors with histological ground truth.

Microglia Activation in the Midbrain of the Human Neonate: The Effect of Perinatal Hypoxic-Ischemic Injury.

Perinatal hypoxia-ischemia (PHI) is a major risk factor for the development of neuropsychiatric deficits later in life. We previously reported that after prolonged PHI, the dopaminergic neurons of the human neonate showed a dramatic reduction of tyrosine hydroxylase (TH) in the substantia nigra, without important signs of neuronal degeneration despite the significant reduction in their cell size. Since microglia activation could precede neuronal death, we now investigated 2 microglia activation markers, ionized calcium-binding adapter molecule 1 (Iba1), and the phagocytosis marker Cd68.

Segmentation of biomedical images based on a computational topology framework.

The homology groups of a topological space provide us with information about its connectivity and the number and type of holes in it. This type of information can find practical applications in describing the intrinsic structure of an image, as well as in identifying equivalence classes in collections of images. When computing homological characteristics, the existence and strength of the relationships between each pair of points in the topological space are studied.

Interrogating the microenvironmental landscape of tumors with computational image analysis approaches.

The tumor microenvironment is an interacting heterogeneous collection of cancer cells, resident as well as infiltrating host cells, secreted factors, and extracellular matrix proteins. With the growing importance of immunotherapies, it has become crucial to be able to characterize the composition and the functional orientation of the microenvironment. The development of novel computational image analysis methodologies may enable the robust quantification and localization of immune and related biomarker-expressing cells within the microenvironment.

CCR5 status and metastatic progression in colorectal cancer.

Multiple reports have highlighted the importance of the local immunological cellular composition (. the density of effector T cells and macrophage polarization state) in predicting clinical outcome in advanced metastatic stage of colorectal cancer. However, in spite of the general association between a high effector T cell density and improved outcome, our recent work has revealed a specific lymphocyte-driven cancer cell-supporting signal.

Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study.

Background: For virtually every patient with colorectal cancer (CRC), hematoxylin-eosin (HE)-stained tissue slides are available. These images contain quantitative information, which is not routinely used to objectively extract prognostic biomarkers. In the present study, we investigated whether deep convolutional neural networks (CNNs) can extract prognosticators directly from these widely available images.

Downregulation of SPARC Is Associated with Epithelial-Mesenchymal Transition and Low Differentiation State of Biliary Tract Cancer Cells.

Background: Biliary tract cancers (BTCs) have a poor prognosis. BTCs are characterized by a prominent desmoplastic reaction which possibly contributes to the aggressive phenotype of this tumor. The desmoplastic reaction includes excessive production and deposition of extracellular matrix proteins such as periostin, secreted protein acidic and rich in cysteine (SPARC), thrombospondin-1, as well as accumulation of α-smooth muscle actin-positive cancer-associated fibroblasts and immune cells, secreting growth factors and cytokines including transforming growth factor (TGF)-β.

Large-scale database mining reveals hidden trends and future directions for cancer immunotherapy.

Cancer immunotherapy has fundamentally changed the landscape of oncology in recent years and significant resources are invested into immunotherapy research. It is in the interests of researchers and clinicians to identify promising and less promising trends in this field in order to rationally allocate resources. This requires a quantitative large-scale analysis of cancer immunotherapy related databases.

Astrocytic glutamine synthetase is expressed in the neuronal somatic layers and down-regulated proportionally to neuronal loss in the human epileptic hippocampus.

Human mesial temporal lobe epilepsy (MTLE) features subregion-specific hippocampal neurodegeneration and reactive astrogliosis, including up-regulation of the glial fibrillary acidic protein (GFAP) and down-regulation of glutamine synthetase (GS). However, the regional astrocytic expression pattern of GFAP and GS upon MTLE-associated neurodegeneration still remains elusive. We assessed GFAP and GS expression in strict correlation with the local neuronal number in cortical and hippocampal surgical specimens from 16 MTLE patients using immunohistochemistry, stereology and high-resolution image analysis for digital pathology and whole-slide imaging.

Modeling of Immunotherapy and Stroma-Targeting Therapies in Human Colorectal Cancer.

Despite the fact that the local immunological microenvironment shapes the prognosis of colorectal cancer, immunotherapy has shown no benefit for the vast majority of colorectal cancer patients. A better understanding of the complex immunological interplay within the microenvironment is required. In this study, we utilized wet lab migration experiments and quantitative histological data of human colorectal cancer tissue samples ( = 20) including tumor cells, lymphocytes, stroma, and necrosis to generate a multiagent spatial model.

Detailed resolution analysis reveals spatial T cell heterogeneity in the invasive margin of colorectal cancer liver metastases associated with improved survival.

On a broader scale, T cell density and localization in colorectal cancer liver metastases have prognostic and predictive implications. As T cell distribution at higher resolutions has not been fully investigated, a detailed resolution analysis of T cell distribution was performed. Patient tissues were divided into 10 µm distance classes between the tumor border and adjacent normal liver.

BMP-9 interferes with liver regeneration and promotes liver fibrosis.

Objective: Bone morphogenetic protein (BMP)-9, a member of the transforming growth factor-β family of cytokines, is constitutively produced in the liver. Systemic levels act on many organs and tissues including bone and endothelium, but little is known about its hepatic functions in health and disease.

Design: Levels of BMP-9 and its receptors were analysed in primary liver cells.

Robust detection and segmentation of cell nuclei in biomedical images based on a computational topology framework.

The segmentation of cell nuclei is an important step towards the automated analysis of histological images. The presence of a large number of nuclei in whole-slide images necessitates methods that are computationally tractable in addition to being effective. In this work, a method is developed for the robust segmentation of cell nuclei in histological images based on the principles of persistent homology.

Spatial intratumoral heterogeneity of proliferation in immunohistochemical images of solid tumors.

Purpose: The interactions of neoplastic cells with each other and the microenvironment are complex. To understand intratumoral heterogeneity, subtle differences should be quantified. Main factors contributing to heterogeneity include the gradient ischemic level within neoplasms, action of microenvironment, mechanisms of intercellular transfer of genetic information, and differential mechanisms of modifications of genetic material/proteins.

VIS-NIR spectroscopy as a process analytical technology for compositional characterization of film biopolymers and correlation with their mechanical properties.

There is an increasing interest in the use of polysaccharides and proteins for the production of biodegradable films. Visible and near-infrared (VIS-NIR) spectroscopy is a reliable analytical tool for objective analyses of biological sample attributes. The objective is to investigate the potential of VIS-NIR spectroscopy as a process analytical technology for compositional characterization of biodegradable materials and correlation to their mechanical properties.

Semantic focusing allows fully automated single-layer slide scanning of cervical cytology slides.

Liquid-based cytology (LBC) in conjunction with Whole-Slide Imaging (WSI) enables the objective and sensitive and quantitative evaluation of biomarkers in cytology. However, the complex three-dimensional distribution of cells on LBC slides requires manual focusing, long scanning-times, and multi-layer scanning. Here, we present a solution that overcomes these limitations in two steps: first, we make sure that focus points are only set on cells.

Multistage histopathological image segmentation of Iba1-stained murine microglias in a focal ischemia model: methodological workflow and expert validation.

A multistage workflow was developed for segmenting and counting murine microglias from histopathological brightfield images, in a permanent focal cerebral ischemia model. Automated counts are useful, since for the assessment of inflammatory mechanisms in ischemic stroke there is a need to quantify the brain's responses to post-ischemia, which primarily is the rapid activation of microglial cells. Permanent middle cerebral artery occlusion was induced in murine brain tissue samples.

Parsimonious classification of binary lacunarity data computed from food surface images using kernel principal component analysis and artificial neural networks.

Lacunarity is about quantifying the degree of spatial heterogeneity in the visual texture of imagery through the identification of the relationships between patterns and their spatial configurations in a two-dimensional setting. The computed lacunarity data can designate a mathematical index of spatial heterogeneity, therefore the corresponding feature vectors should possess the necessary inter-class statistical properties that would enable them to be used for pattern recognition purposes. The objectives of this study is to construct a supervised parsimonious classification model of binary lacunarity data-computed by Valous et al.

Detecting fractal power-law long-range dependence in pre-sliced cooked pork ham surface intensity patterns using Detrended Fluctuation Analysis.

The visual texture of pork ham slices reveals information about the different qualities and perceived image heterogeneity, which is encapsulated as spatial variations in geometry and spectral characteristics. Detrended Fluctuation Analysis (DFA) detects long-range correlations in nonstationary spatial sequences, by a self-similarity scaling exponent alpha. In the current work, the aim is to investigate the usefulness of alpha, using different colour channels (R, G, B, L*, a*, b*, H, S, V, and Grey), as a quantitative descriptor of visual texture in sliced ham surface patterns for the detection of long-range correlations in unidimensional spatial series of greyscale intensity pixel values at 0 degrees , 30 degrees , 45 degrees , 60 degrees , and 90 degrees rotations.

Characterization of fat-connective tissue size distribution in pre-sliced pork hams using multifractal analysis.

Fat-connective tissue size distribution (FSD) in hams is a fundamental physical property for its quality assessment. FSD is related to the sensory properties such as texture, taste, quality of raw meat and visual appearance. In this paper we present a tool to carry out the multifractal analysis (MFA) of two-dimensional binary images of pre-sliced pork hams through the calculation of the f(α)-spectra, Rényi (D(q)) dimensions, and associated statistical regressions and parameters.

Identification of important image features for pork and turkey ham classification using colour and wavelet texture features and genetic selection.

A method to discriminate between various grades of pork and turkey ham was developed using colour and wavelet texture features. Image analysis methods originally developed for predicting the palatability of beef were applied to rapidly identify the ham grade. With high quality digital images of 50-94 slices per ham it was possible to identify the greyscale that best expressed the differences between the various ham grades.

Classification of pre-sliced pork and Turkey ham qualities based on image colour and textural features and their relationships with consumer responses.

Images of three qualities of pre-sliced pork and Turkey hams were evaluated for colour and textural features to characterize and classify them, and to model the ham appearance grading and preference responses of a group of consumers. A total of 26 colour features and 40 textural features were extracted for analysis. Using Mahalanobis distance and feature inter-correlation analyses, two best colour [mean of S (saturation in HSV colour space), std.

Supervised neural network classification of pre-sliced cooked pork ham images using quaternionic singular values.

The quaternionic singular value decomposition is a technique to decompose a quaternion matrix (representation of a colour image) into quaternion singular vector and singular value component matrices exposing useful properties. The objective of this study was to use a small portion of uncorrelated singular values, as robust features for the classification of sliced pork ham images, using a supervised artificial neural network classifier. Images were acquired from four qualities of sliced cooked pork ham typically consumed in Ireland (90 slices per quality), having similar appearances.